1. Field of the Invention
This invention relates generally to the manufacture of high performance integrated circuits on semiconductor devices. More specifically, this invention relates to optimizing the manufacture of high performance integrated circuits on semiconductor devices. Even more specifically, this invention relates to optimizing the manufacture of high performance integrated circuits on semiconductor devices by making appropriate adjustments to the process recipes for each process.
2. Discussion of the Related Art
In the typical semiconductor manufacturing facility, many simulation and analysis tools have been implemented to assist the process integration and device development efforts. These simulation and analysis tools, however, are typically employed to provide an indication of general trends. The latent potential of reducing the number of silicon runs and speeding up the process optimization cycle has not been fully achieved. One of the primary reasons the process optimization cycle has not been achieved is that the accuracy of the data obtained cannot be established to the degree necessary to determine the dependability of the simulation systems. The accuracy of the data obtained can only be achieved by a complete and detailed engineering calibration of the simulation system. This calibration, however, demands extensive engineering resources and data from multiple silicon production runs which, in turn, is usually only available at the latter stages of the process development or early production cycles.
In addition, process optimization for a technology that has completed qualification and is ramping-up production could receive great benefit from the extensive embedded device physics contained in advanced complex simulation tools.
Current trends in semiconductor process development include the use of these simulation tools to predict certain wafer electrical tests (WET) device performance characteristics based on a predetermined set of process values. The use of these simulation tools has been very effective. Additionally, optimal performance of current large-scale integrated devices can be predicted by a subset of critical WET performance parameters. These performance criteria include speed, operating temperature, power utilization, and reliability.
Furthermore, current manufacturing technology utilizes in-line statistical evaluation of critical parametric values at most module steps in the overall process flow. These statistical values are used to maintain control of the process, at the particular process module in question, often without regard to previous processing results. Often the goal of manufacturing is to meet not only yield goals, but certain performance goals as well. Currently, to do this it is necessary to force certain values to meet very strict specifications, such as shifting polysilicon gate critical dimensions (CD) or increasing or increasing threshold adjust implant, and hope that other process module variations will not adversely affect performance.
Further trends in semiconductor processing are continually moving towards more automated or system level control of individual equipment components to maintain a high degree or confidence in the overall process environment. The standard practice has been to require strict adherence to a rigidly controlled specification limit for certain process parameters such that natural variation within those specifications at various critical steps would not permit device performance to vary outside of the desired operating range. The current practice is to continually adjust toward the middle of the specification, making recipe modifications as necessary to implement the fine-tuning, and these adjustments are based on the current trend of the equipment. These specifications stand alone at the individual process steps and are generally not affected by the previous processing results of the lot.
A process simulation tool has been disclosed in the U.S. Pat. No. 5,866,437, entitled DYNAMIC PROCESS WINDOW CONTROL USING SIMULATED WET DATA FROM CURRENT AND PREVIOUS LAYER DATA that uses a method to achieve optimum performance by providing a process control window or specification for the current module by utilizing the previous process step statistical data as a baseline that is entered into the process simulation tool. Such a process control window has the potential of being much wider than current specifications due to the previous layer parameters and their effects being precisely known and can be considered dynamic since the process control window can change based on actual previous layer data. The simulation tool would be preset to optimize the process to hit certain critical WET parametrics. Using the previous data baseline, and the WET goals, the simulator tool would then provide direction by providing a process control window for the remaining operations to achieve those goals. However, there is no current method to automatically adjust the process recipes for each process to incorporate the optimized parameters and account for the current status of the process equipment for each process.
Therefore, what is needed is a system that will evaluate the current trend of each of the process tools and automatically adjust the process recipe of each process.